AI resilience estimate
Your role appears to have useful advantages alongside tasks that may change. Start with one practical experiment.
Higher means more resilience in this estimate. This is not a percentage chance of keeping or losing your job.
Start with one repeatable task. The review below helps you separate routine steps from decisions that need your attention.
Look for moments that need context, trust, or a careful decision. Describe one, then consider what a good outcome would look like.
Based on a typical version of your role. Your actual tasks may differ. Your employer’s plans and local job market are not known.
Your next step · free
Read the reasoning and the six factors.
This is the saved reasoning from an earlier version of the assessment. Its claims were not independently verified. New assessments use the revised method linked below.
Higher means more resilience in this estimate. The weights show each factor’s share of the total. A score of 50 can mean information is unknown; read the note alongside it.
35% of the total
AI coding assistants can now generate boilerplate code, write unit tests, debug simple errors, and explain code functionality. However, software engineering involves much more than writing code: requirements gathering, system architecture, performance optimization, security considerations, code review, debugging complex issues, and cross-team collaboration. Approximately 30-40% of routine coding tasks are automatable, but the higher-level engineering work remains human-dependent. Entry-level engineers face more automation risk than seniors.
20% of the total
AI progress in code generation is extremely rapid. Models like GPT-4, Claude, and specialized tools like AlphaCode have shown dramatic improvements in coding benchmarks. GitHub Copilot adoption has exploded, and new coding agents emerge monthly. However, AI still struggles with novel problem-solving, understanding business context, and maintaining large codebases. The velocity is high but hasn't reached the point of full automation. Expect continued rapid advancement but with diminishing returns on complex engineering tasks.
15% of the total
The software industry is aggressively adopting AI coding tools. GitHub reports over 1 million Copilot subscribers, and most major tech companies have integrated AI assistants into development workflows. However, large enterprises (5000+ employees) move more cautiously due to security, compliance, and legacy system constraints. Adoption is high for augmentation tools but low for full automation. The industry is investing heavily in AI while simultaneously hiring engineers to build and maintain these systems.
15% of the total
Software engineering retains strong human advantages: creative problem-solving for novel challenges, understanding business requirements and user needs, making architectural tradeoffs, collaborating across teams, mentoring junior developers, and exercising judgment on security and ethical implications. Engineers must navigate ambiguity, prioritize features, and balance technical debt. At 0 years experience, you're still developing these skills, but they become increasingly valuable. The social and strategic aspects of engineering are deeply human.
10% of the total
Software engineering skills are highly transferable across industries and roles. Programming fundamentals, problem-solving, logical thinking, and technical communication apply to data science, product management, DevOps, cybersecurity, and emerging AI-related roles. Even at 0 years experience, you're building foundational skills that translate broadly. The ability to learn new technologies quickly is itself a meta-skill. Software engineers can pivot to adjacent technical roles more easily than most professions.
5% of the total
Despite recent tech layoffs, software engineering remains one of the most in-demand professions globally. The U.S. Bureau of Labor Statistics projects 25% growth for software developers through 2032, much faster than average. Entry-level positions are more competitive post-2023, but demand remains strong. AI is creating new engineering specializations (ML engineers, AI safety researchers, LLM fine-tuners) while traditional software needs continue growing. Salaries remain high, and remote opportunities are abundant.
This is the saved reasoning from an earlier version of the assessment. Its claims were not independently verified. New assessments use the revised method at https://www.ai-proof.me/methodology.
Higher means more resilience in this estimate. The weights show each factor’s share of the total. A score of 50 can mean information is unknown; read the note alongside it.
35% of the total
AI coding assistants can now generate boilerplate code, write unit tests, debug simple errors, and explain code functionality. However, software engineering involves much more than writing code: requirements gathering, system architecture, performance optimization, security considerations, code review, debugging complex issues, and cross-team collaboration. Approximately 30-40% of routine coding tasks are automatable, but the higher-level engineering work remains human-dependent. Entry-level engineers face more automation risk than seniors.
20% of the total
AI progress in code generation is extremely rapid. Models like GPT-4, Claude, and specialized tools like AlphaCode have shown dramatic improvements in coding benchmarks. GitHub Copilot adoption has exploded, and new coding agents emerge monthly. However, AI still struggles with novel problem-solving, understanding business context, and maintaining large codebases. The velocity is high but hasn't reached the point of full automation. Expect continued rapid advancement but with diminishing returns on complex engineering tasks.
15% of the total
The software industry is aggressively adopting AI coding tools. GitHub reports over 1 million Copilot subscribers, and most major tech companies have integrated AI assistants into development workflows. However, large enterprises (5000+ employees) move more cautiously due to security, compliance, and legacy system constraints. Adoption is high for augmentation tools but low for full automation. The industry is investing heavily in AI while simultaneously hiring engineers to build and maintain these systems.
15% of the total
Software engineering retains strong human advantages: creative problem-solving for novel challenges, understanding business requirements and user needs, making architectural tradeoffs, collaborating across teams, mentoring junior developers, and exercising judgment on security and ethical implications. Engineers must navigate ambiguity, prioritize features, and balance technical debt. At 0 years experience, you're still developing these skills, but they become increasingly valuable. The social and strategic aspects of engineering are deeply human.
10% of the total
Software engineering skills are highly transferable across industries and roles. Programming fundamentals, problem-solving, logical thinking, and technical communication apply to data science, product management, DevOps, cybersecurity, and emerging AI-related roles. Even at 0 years experience, you're building foundational skills that translate broadly. The ability to learn new technologies quickly is itself a meta-skill. Software engineers can pivot to adjacent technical roles more easily than most professions.
5% of the total
Despite recent tech layoffs, software engineering remains one of the most in-demand professions globally. The U.S. Bureau of Labor Statistics projects 25% growth for software developers through 2032, much faster than average. Entry-level positions are more competitive post-2023, but demand remains strong. AI is creating new engineering specializations (ML engineers, AI safety researchers, LLM fine-tuners) while traditional software needs continue growing. Salaries remain high, and remote opportunities are abundant.
Receive a monthly reminder of checked resources. We do not invent news or treat a new score as progress.
Weekly emails, every Tuesday. Unsubscribe anytime with one click.